US20100049096A1 - System for fall prevention and a method for fall prevention using such a system - Google Patents

System for fall prevention and a method for fall prevention using such a system Download PDF

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Publication number
US20100049096A1
US20100049096A1 US12/513,508 US51350807A US2010049096A1 US 20100049096 A1 US20100049096 A1 US 20100049096A1 US 51350807 A US51350807 A US 51350807A US 2010049096 A1 US2010049096 A1 US 2010049096A1
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Prior art keywords
postures
lower body
body segment
sequence
user
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Abandoned
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US12/513,508
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Warner Rudolph Theophile Ten Kate
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Koninklijke Philips NV
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Koninklijke Philips Electronics NV
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Assigned to KONINKLIJKE PHILIPS ELECTRONICS N. V. reassignment KONINKLIJKE PHILIPS ELECTRONICS N. V. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TEN KATE, WARNER RUDOLPH THEOPHILE
Publication of US20100049096A1 publication Critical patent/US20100049096A1/en
Abandoned legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for

Definitions

  • the invention relates to a system for fall prevention for a user.
  • an accelerometer for instance worn in a housing connected to the belt of the user.
  • the accelerometer triggers on high impact and/or free-fall acceleration. Additional parameters for refining the triggering could be detecting horizontal position and duration of staying in said position after an incident. After an incident, like falling, occurs, the accelerometer can warn a service centre, which calls back the user over a telephone line and subsequently decides about actions to take in order to help a user.
  • the system comprises a number of sensors attachable to at least one lower body segment, wherein said sensors are adapted to measure movement of said at least one lower body segment and to translate the movement into a signal, the system further comprising a control adapted to receive the signal from said respective sensors, wherein in use the control observes the signal as an actual sequence of postures of said at least one lower body segment and compares the actual sequence with a predetermined sequence of postures as a function of time, wherein the control is adapted to determine a high risk of falling when the actual sequence deviates from the predetermined sequence in a certain way.
  • the system Due to the change in sequence of postures over time in relation to a known sequence that represents a low risk of falling, the system is able to accurately detect (temporarily) higher risk of falling. This results in a dynamic way of monitoring a user during movement, for instance during walking, over a period of time.
  • the system is able to detect a situation of imbalance of the user on time such that the user or a care provider can take precautions. For instance, when a user is not paying full attention to the walking because he is talking, listening to the radio etc., the movement of the person can provide a higher risk of falling, which is detected by the system and warns the user.
  • another person for instance a nurse, can be alerted when a higher risk of falling is indicated by the system. In that case, the nurse can accompany said user in order to prevent him from falling.
  • the posture of the lower body segment is determined by the position of lower body segment parts relative to each other.
  • the lower body segment parts preferably comprise an ankle, a foot, a knee, a lower leg, an upper leg, a hip of a similar lower body segment and/or a trunk.
  • a comparison of the actual sequence of postures to the predetermined sequence of postures of the lower body segment is performed with aid of an adaptive algorithm, for example a neural network or a support vector machine.
  • an adaptive algorithm for example a neural network or a support vector machine.
  • the system is configured to monitor a muscle strength or power of muscles of the lower body segment, e.g., using EMG, and configured to use a detected muscle strength or power in the determining of the high risk of falling.
  • Muscle strength or power relates to the balance of a user, i.e. the stability of the mechanical system of the user. Thus, detection of muscle strength or power contributes to indicating the risk of falling.
  • the predetermined sequence of postures of the lower body segment is determined by measuring successive lower body segment postures during normal movement of the user and the amount of variation therein. By doing so, the system learns a normal sequence of postures of at least one lower body segment when a person is moving, for instance walking, with a low risk of falling. By also measuring the amount of variation in the sequence, the system learns to what extent the normal sequence is staying within the level of low risk of falling, thereby preventing to warn the user to often or without needing to.
  • the deviation of the actual sequence of postures in relation to the predetermined sequence of postures is based on the increase or decrease in variation in the sequence as a function of time.
  • the high risk of falling is determined by a deviation threshold that is estimated from a mean and the variation by classifying the actual sequence of postures. For instance, a mean of the signals is determined and the trend therein is monitored. When a deviation in the means occurs, a signal is generated to warn a user or another person. For example, when a user becomes fatigue not only a single movement is influenced. By using the deviation in the mean of the signals, the degree of fatigueness is represented in the trend in movement.
  • the system is adapted to provide a warning signal, during walking, when the high risk of falling has been determined.
  • a warning signal can be given to the user wearing the system for fall prevention, but can also be given to for instance a caretaker of the user, such as a nurse. The caretaker is then able to help the user in order decrease the high risk of falling at that time.
  • the warning signal can be an audible signal or a visual signal, like a warning text on a display or a flashing light.
  • the system comprises a memory for storing the sequence of postures of the at least one lower body segment.
  • a memory for storing the sequence of postures of the at least one lower body segment.
  • Such a memory enables the predetermined sequence of postures being dynamical by storing latest sequences in the memory and by recalibrating the adaptive algorithm occasionally, by using the sequences available in the memory at that time.
  • sequences in alarm situations are removed from the memory. These sequences can however be collected and used to train the algorithm to learn a category of risk patterns.
  • the adaptive algorithm is self-learning by adaptation of the predetermined sequence of postures in case of changing conditions of the user.
  • the system first gradually learns the normal walking pattern of the user in order to be able to differentiate between a normal and a dangerous pattern.
  • the algorithm learns that the changed patterns are the normal sequence of postures.
  • the system is configured to monitor an angle between a lower leg and an upper leg of the user, to determine whether a high risk of falling is reached during walking of the user.
  • a high risk of falling is reached during walking of the user.
  • the senor is one of an accelerometer, a gyroscope or a magnetometer. These sensors enable easy detection of the posture of the upper leg-lower leg system.
  • the sensor may be miniature and/or wireless sensors, such that it is not inconvenient for the user wearing said sensors.
  • the sensors can be adapted to continuously measure the relative posture of the lower body segment parts. It is also possible that other kinds of sensors can be used to determine the posture of the upper leg-lower leg system.
  • the predetermined sequence of postures can be determined by entering parameters into the control. Instead of training and tracking the actual sequences of postures of the lower body segment, it is then possible to train and track on the sequences determined by the entered parameters.
  • the parameters can be chosen from, but is not restricted to, the group of: an amount of knee-bending over a certain time period, an average of knee-bending over a certain time period, a range of amount of knee-bending over a certain time period, a variation of the amount of knee bending over a certain time period, a step size, a left (right) knee stretching in response to right (left) knee bending.
  • the invention further relates to a method for fall prevention for a user, using an above described system, wherein movement of at least one lower body segment is measured and translated into a signal, wherein successive signals are translated into an actual sequence of postures of said at least one lower body segment, wherein the actual sequence is compared with a predetermined sequence of postures over a certain time period, wherein a high risk of falling is being indicated when the actual sequence deviates from the predetermined sequence to a certain degree.
  • a method for fall prevention provides similar advantages and effects as are mentioned with the description of the system for fall prevention.
  • FIG. 1 shows a mechanical system of the lower body segment comprising sensors
  • FIG. 2 shows a diagram of a system according to an embodiment of the invention.
  • FIG. 1 illustrates a system for fall prevention for a user.
  • a number of sensors 2 is attached to a lower body segment 3 , for example a leg of a user.
  • the sensors 2 are adapted to measure movement of the lower body segment 3 and to translate said movement into a signal S.
  • the signal S of the sensors 2 is received by a control 12 .
  • the control 12 translates the signal into an actual sequence of postures of the lower body segment 3 .
  • the signal S is converted into an actual sequence of postures at operation 100 .
  • the actual sequence of postures is then compared by control 12 with a predetermined sequence of postures as a function of time, wherein the predetermined sequence relates to a low risk of falling or the usual risk for that user.
  • the control 12 is further adapted to determine a high risk of falling when the actual sequence deviates from the predetermined sequence to a certain degree.
  • the comparison of the actual sequence of postures to the predetermined sequence of postures of the lower body segment 3 is performed with aid of an adaptive algorithm 11 , for example a neural network or a support vector machine.
  • the predetermined sequence can be stored in a memory 10 of the system.
  • the adaptive algorithm 11 can be configured with preset coefficients, in which case storage in the memory 10 and operation 110 is not required. However, better performance can be obtained when the coefficients are trained, through operation 110 , from the predetermined sequences stored in the memory 10 . This allows for a better comparison result with the actual pattern. Also, if the user alters his/her normal movement patterns, the algorithm 11 can adapt to those patterns through a new learning cycle 110 .
  • FIG. 1 a mechanical system of the lower body segment 3 is shown.
  • the posture of the lower body segment 3 is determined by the position of at least two lower body segment parts 6 , 7 relative to each other.
  • the lower body segment parts can be two of the following: foot 9 , ankle 8 , lower leg 6 , knee 5 , upper leg 7 , hip 4 , and/or trunk (not shown).
  • Three sensors 2 are provided on respectively the ankle 8 , knee 5 and hip 4 of a person in order to perform a positional measurement of that lower body segment 3 . From said positions the body segment's angle can be computed.
  • accelerometers 2 are attached to the upper leg 7 and lower leg 6 of both legs, such that the posture of the legs can be computed as a function of time. Also additional sensors for calibration purposes can be provided (not shown). Sensors 2 can be placed on one leg or on both legs. When the user walks a trajectory, the sequence of postures of both legs can be sampled and stored in the memory 10 . The sequence is used to adapt the adaptive algorithm 11 .
  • the predetermined sequence is used during operation of the system 1 for fall prevention.
  • the actual sequence of postures of the lower body segment 3 is monitored, during walking, and compared with the sequences that the algorithm 11 is trained with, e.g. through the sequences that are stored in the memory 10 (at operation 110 ). If the actual sequence of postures deviates from the predetermined sequence, i.e. the actual pattern is not recognized to match one of the patterns stored in the memory 10 , the user is warned for instance with a warning signal (operation 130 ), for example via a loudspeaker 131 or in a different way. If the deviation is relatively small, there is low risk of falling (operation 140 ) and the user is not alerted.
  • the system 1 can, instead of giving a warning signal, provide the user with an advice, for instance taking a break etc.
  • the algorithm 11 can also compute statistical parameters such as mean and variance of the actual sequence. These numbers can be compared with those of the earlier sequences stored in the memory 10 . This comparison is done in a comparator 120 . If the actual mean or variance surpasses a deviation threshold relative to those from the earlier sequences, the user is warned for instance with a warning signal (operation 130 ), for example via a loudspeaker 131 or in a different way. If the deviation is relatively small, there is low risk of falling (operation 140 ) and the user is not alerted.
  • Adaptation of the adaptive algorithm 11 is focused on learning normal situations and developing a variation therein.
  • a deviation threshold can be estimated form the mean and variation in classifying the normal sequences. It is assumed that an insignificant number of sequences of high-risk situations is available, therefore the adaptive algorithm 11 is adapted to learn a reliable classification of risk situations.
  • the adaptive algorithm 11 does not classify the sequences but it returns a degree of fitting into the classification, i.e. the distance to the mean of the class. This distance is compared with the spread of learning samples in said class. It is also possible that the adaptive algorithm 11 is adapted to perform a clustering with the sequences of the postures in the memory 10 together with the actual sequence of postures. If the actual sequence is put in a different cluster than the predetermined sequences, a situation of high risk for falling is detected.
  • the predetermined sequences can be dynamic in the sense that they can be adapted, for instance due to a change in the user's conditions. Therefore, the latest actual sequences are stored in the memory 10 and the adaptive algorithm 11 is recalibrated once a while, using the latest actual sequences from the memory 10 . Alarmed situations can be removed from the memory 10 and can be collected in order to learn the algorithm a category of risk sequences.
  • the above-described system for fall prevention provides a simple and inexpensive way of preventing a user for falling. Furthermore, the system is very accurate and can take into account behaviour of a user that creates a higher risk of falling.
  • sensors are placed on both lower body segments to determine the sequence of postures of both legs at the same time, thereby providing an accurate fall prevention system.
  • sensors are applied to determine sequences of body segment postures during steady state phases of movement.
  • the sequences of lower body segment postures can provide accurate information concerning the risk of falling, since the balance of a user is (mostly) dependent on the system of hip, knee and ankle.
  • the balance of a user is (mostly) dependent on the system of hip, knee and ankle.
  • knee buckling when a user is getting tired, it gets harder to normally stretch the knee (often referred to as knee buckling).
  • knee buckling when it is harder for a user to stay balanced, it is associated with a larger sway (movement of the hips ).
  • Another, often used model of balance is the inverted pendulum, taking the ankle as a pivoting point.

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US12/513,508 2006-11-14 2007-11-09 System for fall prevention and a method for fall prevention using such a system Abandoned US20100049096A1 (en)

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EP06124031.3 2006-11-14
EP06124031 2006-11-14
PCT/IB2007/054560 WO2008059418A1 (en) 2006-11-14 2007-11-09 System for fall prevention and a method for fall prevention using such a system

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EP (1) EP2084688A1 (pt)
JP (1) JP2010508945A (pt)
KR (1) KR20090077823A (pt)
CN (1) CN101536053A (pt)
BR (1) BRPI0718640A2 (pt)
RU (1) RU2009122475A (pt)
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US20100109894A1 (en) * 2008-10-31 2010-05-06 Chi Mei Communication Systems, Inc. Electronic device and method to prevent falling of the electronic device
US20110152727A1 (en) * 2008-09-04 2011-06-23 Koninklijke Philips Electronics N.V. Fall prevention system
US20110218460A1 (en) * 2010-03-08 2011-09-08 Seiko Epson Corporation Fall detecting device and fall detecting method
US20110264008A1 (en) * 2010-04-21 2011-10-27 National Chiao Tung University Apparatus for identifying falls and activities of daily living
US20120172681A1 (en) * 2010-12-30 2012-07-05 Stmicroelectronics R&D (Beijing) Co. Ltd Subject monitor
US20140303460A1 (en) * 2011-11-11 2014-10-09 University Of Limerick System for management and prevention of venous pooling
US20150320339A1 (en) * 2011-10-03 2015-11-12 Centauri Medical Inc. System and method for analyzing patient orientation, location and movement
US20160220153A1 (en) * 2013-09-11 2016-08-04 Koninklijke Philips N.V. Fall detection system and method
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US10874330B2 (en) 2010-03-07 2020-12-29 Leaf Healthcare, Inc. Systems, devices and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US10912491B2 (en) 2010-04-22 2021-02-09 Leaf Healthcare, Inc. Systems, devices and methods for managing pressurization timers for monitoring and/or managing a person's position
US10930131B2 (en) 2017-06-28 2021-02-23 Koninklijke Philips N.V. Method and apparatus for providing feedback to a user about a fall risk
US11051751B2 (en) 2010-04-22 2021-07-06 Leaf Healthcare, Inc. Calibrated systems, devices and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US11183304B2 (en) 2019-01-08 2021-11-23 International Business Machines Corporation Personalized smart home recommendations through cognitive load analysis
US11272860B2 (en) 2010-04-22 2022-03-15 Leaf Healthcare, Inc. Sensor device with a selectively activatable display
US11278237B2 (en) 2010-04-22 2022-03-22 Leaf Healthcare, Inc. Devices, systems, and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US11369309B2 (en) 2010-04-22 2022-06-28 Leaf Healthcare, Inc. Systems and methods for managing a position management protocol based on detected inclination angle of a person
US11379047B2 (en) * 2019-06-25 2022-07-05 Lifeline Systems Company Evaluating movement of a subject
US11640756B2 (en) * 2019-06-26 2023-05-02 Lifeline Systems Company Monitoring a subject
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WO2014032181A1 (en) 2012-08-27 2014-03-06 Université Du Québec À Chicoutimi Method to determine physical properties of the ground, foot-worn sensor therefore, and method to advise a user of a risk of falling based thereon
ES2448941B1 (es) * 2012-09-17 2015-02-10 Mª Del Carmen LOPEZ GONZALEZ Método y sistema de alarma para detectar posturas incorrectas
CN103211599A (zh) * 2013-05-13 2013-07-24 桂林电子科技大学 一种监测跌倒的方法及装置
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JP6183906B2 (ja) * 2013-08-28 2017-08-23 日本電信電話株式会社 歩容推定装置とそのプログラム、転倒危険度算出装置とそのプログラム
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US9392966B2 (en) * 2008-09-04 2016-07-19 Koninklijke Philips N.V. Fall prevention system
US20110152727A1 (en) * 2008-09-04 2011-06-23 Koninklijke Philips Electronics N.V. Fall prevention system
US8258972B2 (en) * 2008-10-31 2012-09-04 Chi Mei Communication Systems, Inc. Electronic device and method to prevent falling of the electronic device
US20100109894A1 (en) * 2008-10-31 2010-05-06 Chi Mei Communication Systems, Inc. Electronic device and method to prevent falling of the electronic device
US10874330B2 (en) 2010-03-07 2020-12-29 Leaf Healthcare, Inc. Systems, devices and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US20110218460A1 (en) * 2010-03-08 2011-09-08 Seiko Epson Corporation Fall detecting device and fall detecting method
US20110264008A1 (en) * 2010-04-21 2011-10-27 National Chiao Tung University Apparatus for identifying falls and activities of daily living
US8974403B2 (en) 2010-04-21 2015-03-10 National Chiao Tung University Apparatus for identifying falls and activities of daily living
US11980449B2 (en) 2010-04-22 2024-05-14 Leaf Healthcare, Inc. Systems and methods for monitoring orientation and biometric data using acceleration data
US11278237B2 (en) 2010-04-22 2022-03-22 Leaf Healthcare, Inc. Devices, systems, and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US10888251B2 (en) 2010-04-22 2021-01-12 Leaf Healthcare, Inc. Systems, devices and methods for analyzing the attachment of a wearable sensor device on a user
US10912491B2 (en) 2010-04-22 2021-02-09 Leaf Healthcare, Inc. Systems, devices and methods for managing pressurization timers for monitoring and/or managing a person's position
US11948681B2 (en) 2010-04-22 2024-04-02 Leaf Healthcare, Inc. Wearable sensor device and methods for analyzing a persons orientation and biometric data
US11883154B2 (en) 2010-04-22 2024-01-30 Leaf Healthcare, Inc. Systems and methods for monitoring a person's position
US11369309B2 (en) 2010-04-22 2022-06-28 Leaf Healthcare, Inc. Systems and methods for managing a position management protocol based on detected inclination angle of a person
US11317830B2 (en) 2010-04-22 2022-05-03 Leaf Healthcare, Inc. Systems and methods for managing pressurization timers for monitoring and/or managing a person's position
US11051751B2 (en) 2010-04-22 2021-07-06 Leaf Healthcare, Inc. Calibrated systems, devices and methods for preventing, detecting, and treating pressure-induced ischemia, pressure ulcers, and other conditions
US11272860B2 (en) 2010-04-22 2022-03-15 Leaf Healthcare, Inc. Sensor device with a selectively activatable display
US20120172681A1 (en) * 2010-12-30 2012-07-05 Stmicroelectronics R&D (Beijing) Co. Ltd Subject monitor
US20150320339A1 (en) * 2011-10-03 2015-11-12 Centauri Medical Inc. System and method for analyzing patient orientation, location and movement
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CN101536053A (zh) 2009-09-16
WO2008059418A1 (en) 2008-05-22
JP2010508945A (ja) 2010-03-25
EP2084688A1 (en) 2009-08-05
KR20090077823A (ko) 2009-07-15
BRPI0718640A2 (pt) 2013-11-26

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